Angelfish (Pterophyllum scalare) discriminate between small quantities: A role of memory.
Bibliographic record
Abstract
The ability to discriminate between sets of items differing in quantity has shown a growing interest in comparative studies as a diversity of animal species exhibit such quantitative competence. Previous studies with angelfish (Pterophyllum scalare) have demonstrated that this species is capable of spontaneously discriminating between fully visible groups (shoals) of conspecifics of different numerical size. In the present study, we investigated quantity discrimination in angelfish adopting a new procedure that we expected to make the task more difficult for the fish. During a pretest period, angelfish were allowed to fully see shoals of conspecifics of different numerical size, subsequently all fish but 1 in each stimulus shoal were hidden behind opaque barriers. Thus, during testing, experimental fish had to rely on their working memory, which implies a certain level of mental representation of the quantities or numbers discriminated. Angelfish chose the larger shoal with similar accuracy when 1 versus 2, 1 versus 3, 1 versus 4, 2 versus 3, and 2 versus 4 stimulus fish were contrasted, but failed to distinguish shoals when 3 versus 4, 4 versus 5, and 4 versus 6 fish were contrasted. Strong similarities were observed in relation with our previous procedure indicating the robustness of the quantity discrimination abilities of this species. Our results imply that angelfish form internal representations and demonstrate that these fish can make comparisons between small quantities of items while relying on their working memory alone.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".